AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	logloss	1.04705	1.49
	2_DecisionTree	Decision Tree	logloss	0.0176364	26.92
the best	3_Default_Xgboost	Xgboost	logloss	0.000281915	32.04
	4_Default_NeuralNetwork	Neural Network	logloss	0.0176435	12.45
	5_Default_RandomForest	Random Forest	logloss	0.00309481	33.2
	Ensemble	Ensemble	logloss	0.000281915	0.81

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of Ensemble

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Ensemble structure

Model	Weight
3_Default_Xgboost	3

Metric details

	Elective	Emergency	Newborn	Not Available	Trauma	Urgent	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	1	1	0.000281915
recall	1	1	1	1	1	1	1	1	1	0.000281915
f1-score	1	1	1	1	1	1	1	1	1	0.000281915
support	845	2807	428	5	12	327	1	4424	4424	0.000281915

Confusion matrix

	Predicted as Elective	Predicted as Emergency	Predicted as Newborn	Predicted as Not Available	Predicted as Trauma	Predicted as Urgent
Labeled as Elective	845	0	0	0	0	0
Labeled as Emergency	0	2807	0	0	0	0
Labeled as Newborn	0	0	428	0	0	0
Labeled as Not Available	0	0	0	5	0	0
Labeled as Trauma	0	0	0	0	12	0
Labeled as Urgent	0	0	0	0	0	327

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 2_DecisionTree

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Decision Tree

• n_jobs: -1
• criterion: gini
• max_depth: 3
• num_class: 6
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

25.8 seconds

Metric details

	Elective	Emergency	Newborn	Not Available	Trauma	Urgent	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	0	0	0.950581	0.996157	0.65843	0.992505	0.0176364
recall	1	1	1	0	0	1	0.996157	0.666667	0.996157	0.0176364
f1-score	1	1	1	0	0	0.974665	0.996157	0.662444	0.994285	0.0176364
support	845	2807	428	5	12	327	0.996157	4424	4424	0.0176364

Confusion matrix

	Predicted as Elective	Predicted as Emergency	Predicted as Newborn	Predicted as Not Available	Predicted as Trauma	Predicted as Urgent
Labeled as Elective	845	0	0	0	0	0
Labeled as Emergency	0	2807	0	0	0	0
Labeled as Newborn	0	0	428	0	0	0
Labeled as Not Available	0	0	0	0	0	5
Labeled as Trauma	0	0	0	0	0	12
Labeled as Urgent	0	0	0	0	0	327

Learning curves

Decision Tree

Tree #1

Rules

if (Type of Admission > 0.5) and (Type of Admission <= 1.5) then class: Emergency (proba: 100.0%) | based on 8,419 samples

if (Type of Admission <= 0.5) then class: Elective (proba: 100.0%) | based on 2,536 samples

if (Type of Admission > 0.5) and (Type of Admission > 1.5) and (Type of Admission <= 2.5) then class: Newborn (proba: 100.0%) | based on 1,286 samples

if (Type of Admission > 0.5) and (Type of Admission > 1.5) and (Type of Admission > 2.5) then class: Urgent (proba: 95.15%) | based on 1,030 samples

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence Elective (Fold 1)

Dependence Emergency (Fold 1)

Dependence Newborn (Fold 1)

Dependence Not Available (Fold 1)

Dependence Trauma (Fold 1)

Dependence Urgent (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 3_Default_Xgboost

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Extreme Gradient Boosting (Xgboost)

• n_jobs: -1
• objective: multi:softprob
• eta: 0.075
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• eval_metric: mlogloss
• num_class: 6
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

30.8 seconds

Metric details

	Elective	Emergency	Newborn	Not Available	Trauma	Urgent	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	1	1	1	1	1	1	0.000281915
recall	1	1	1	1	1	1	1	1	1	0.000281915
f1-score	1	1	1	1	1	1	1	1	1	0.000281915
support	845	2807	428	5	12	327	1	4424	4424	0.000281915

Confusion matrix

	Predicted as Elective	Predicted as Emergency	Predicted as Newborn	Predicted as Not Available	Predicted as Trauma	Predicted as Urgent
Labeled as Elective	845	0	0	0	0	0
Labeled as Emergency	0	2807	0	0	0	0
Labeled as Newborn	0	0	428	0	0	0
Labeled as Not Available	0	0	0	5	0	0
Labeled as Trauma	0	0	0	0	12	0
Labeled as Urgent	0	0	0	0	0	327

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence Elective (Fold 1)

Dependence Emergency (Fold 1)

Dependence Newborn (Fold 1)

Dependence Not Available (Fold 1)

Dependence Trauma (Fold 1)

Dependence Urgent (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 4_Default_NeuralNetwork

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Neural Network

• n_jobs: -1
• dense_1_size: 32
• dense_2_size: 16
• learning_rate: 0.05
• num_class: 6
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

11.5 seconds

Metric details

	Elective	Emergency	Newborn	Not Available	Trauma	Urgent	accuracy	macro avg	weighted avg	logloss
precision	1	0.997866	0.993023	1	0.5	0.981873	0.995705	0.912127	0.995275	0.0176435
recall	0.995266	0.999644	0.997664	0.4	0.333333	0.993884	0.995705	0.786632	0.995705	0.0176435
f1-score	0.997628	0.998754	0.995338	0.571429	0.4	0.987842	0.995705	0.825165	0.995295	0.0176435
support	845	2807	428	5	12	327	0.995705	4424	4424	0.0176435

Confusion matrix

	Predicted as Elective	Predicted as Emergency	Predicted as Newborn	Predicted as Not Available	Predicted as Trauma	Predicted as Urgent
Labeled as Elective	841	4	0	0	0	0
Labeled as Emergency	0	2806	1	0	0	0
Labeled as Newborn	0	1	427	0	0	0
Labeled as Not Available	0	0	0	2	3	0
Labeled as Trauma	0	1	1	0	4	6
Labeled as Urgent	0	0	1	0	1	325

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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Summary of 5_Default_RandomForest

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Random Forest

• n_jobs: -1
• criterion: gini
• max_features: 0.9
• min_samples_split: 30
• max_depth: 4
• eval_metric_name: logloss
• num_class: 6
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

31.8 seconds

Metric details

	Elective	Emergency	Newborn	Not Available	Trauma	Urgent	accuracy	macro avg	weighted avg	logloss
precision	1	1	1	0	0.705882	1	0.99887	0.784314	0.998072	0.00309481
recall	1	1	1	0	1	1	0.99887	0.833333	0.99887	0.00309481
f1-score	1	1	1	0	0.827586	1	0.99887	0.804598	0.998402	0.00309481
support	845	2807	428	5	12	327	0.99887	4424	4424	0.00309481

Confusion matrix

	Predicted as Elective	Predicted as Emergency	Predicted as Newborn	Predicted as Not Available	Predicted as Trauma	Predicted as Urgent
Labeled as Elective	845	0	0	0	0	0
Labeled as Emergency	0	2807	0	0	0	0
Labeled as Newborn	0	0	428	0	0	0
Labeled as Not Available	0	0	0	0	5	0
Labeled as Trauma	0	0	0	0	12	0
Labeled as Urgent	0	0	0	0	0	327

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

SHAP Importance

SHAP Dependence plots

Dependence Elective (Fold 1)

Dependence Emergency (Fold 1)

Dependence Newborn (Fold 1)

Dependence Not Available (Fold 1)

Dependence Trauma (Fold 1)

Dependence Urgent (Fold 1)

SHAP Decision plots

Worst decisions for selected sample 1 (Fold 1)

Worst decisions for selected sample 2 (Fold 1)

Worst decisions for selected sample 3 (Fold 1)

Worst decisions for selected sample 4 (Fold 1)

Best decisions for selected sample 1 (Fold 1)

Best decisions for selected sample 2 (Fold 1)

Best decisions for selected sample 3 (Fold 1)

Best decisions for selected sample 4 (Fold 1)

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Summary of 1_Baseline

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Baseline Classifier (Baseline)

• n_jobs: -1
• num_class: 6
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

0.8 seconds

Metric details

	Elective	Emergency	Newborn	Not Available	Trauma	Urgent	accuracy	macro avg	weighted avg	logloss
precision	0	0.634494	0	0	0	0	0.634494	0.105749	0.402582	1.04705
recall	0	1	0	0	0	0	0.634494	0.166667	0.634494	1.04705
f1-score	0	0.776379	0	0	0	0	0.634494	0.129397	0.492608	1.04705
support	845	2807	428	5	12	327	0.634494	4424	4424	1.04705

Confusion matrix

	Predicted as Elective	Predicted as Emergency	Predicted as Newborn	Predicted as Not Available	Predicted as Trauma	Predicted as Urgent
Labeled as Elective	0	845	0	0	0	0
Labeled as Emergency	0	2807	0	0	0	0
Labeled as Newborn	0	428	0	0	0	0
Labeled as Not Available	0	5	0	0	0	0
Labeled as Trauma	0	12	0	0	0	0
Labeled as Urgent	0	327	0	0	0	0

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Precision Recall Curve

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